Single-cell DNA methylome and 3D multi-omic atlas of the adult mouse brain.
Journal
Nature
ISSN: 1476-4687
Titre abrégé: Nature
Pays: England
ID NLM: 0410462
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
received:
08
04
2023
accepted:
31
10
2023
medline:
14
12
2023
pubmed:
14
12
2023
entrez:
13
12
2023
Statut:
ppublish
Résumé
Cytosine DNA methylation is essential in brain development and is implicated in various neurological disorders. Understanding DNA methylation diversity across the entire brain in a spatial context is fundamental for a complete molecular atlas of brain cell types and their gene regulatory landscapes. Here we used single-nucleus methylome sequencing (snmC-seq3) and multi-omic sequencing (snm3C-seq)
Identifiants
pubmed: 38092913
doi: 10.1038/s41586-023-06805-y
pii: 10.1038/s41586-023-06805-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
366-377Informations de copyright
© 2023. The Author(s).
Références
Lee, D.-S. et al. Simultaneous profiling of 3D genome structure and DNA methylation in single human cells. Nat. Methods 16, 999–1006 (2019).
pubmed: 31501549
pmcid: 6765423
doi: 10.1038/s41592-019-0547-z
Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).
pubmed: 24056875
doi: 10.1038/nmeth.2639
Wang, Q. et al. The Allen Mouse Brain Common Coordinate Framework: a 3D reference atlas. Cell 181, 936–953.e20 (2020).
pubmed: 32386544
pmcid: 8152789
doi: 10.1016/j.cell.2020.04.007
Yao, Z. et al. A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex. Nature 598, 103–110 (2021).
pubmed: 34616066
pmcid: 8494649
doi: 10.1038/s41586-021-03500-8
Yao, Z. et al. A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation. Cell https://doi.org/10.1016/j.cell.2021.04.021 (2021).
Yao, Z. et al. A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain. Nature https://doi.org/10.1038/s41586-023-06812-z (2023).
Zhang, M. et al. Molecularly defined and spatially resolved cell atlas of the whole mouse brain. Nature https://doi.org/10.1038/s41586-023-06808-9 (2023).
Langlieb, J. et al. The molecular cytoarchitecture of the adultmouse brain. Nature https://doi.org/10.1038/s41586-023-06818-7 (2023).
Liu, H. et al. DNA methylation atlas of the mouse brain at single-cell resolution. Nature 598, 120–128 (2021).
pubmed: 34616061
pmcid: 8494641
doi: 10.1038/s41586-020-03182-8
Li, Y. E. et al. An atlas of gene regulatory elements in adult mouse cerebrum. Nature 598, 129–136 (2021).
pubmed: 34616068
pmcid: 8494637
doi: 10.1038/s41586-021-03604-1
Zu, S. et al. Single-cell analysis of chromatinaccessibility in the adult mouse brain. Nature https://doi.org/10.1038/s41586-023-06824-9 (2023).
Herring, C. A. et al. Human prefrontal cortex gene regulatory dynamics from gestation to adulthood at single-cell resolution. Cell https://doi.org/10.1016/j.cell.2022.09.039 (2022).
Armand, E. J., Li, J., Xie, F., Luo, C. & Mukamel, E. A. Single-cell sequencing of brain cell transcriptomes and epigenomes. Neuron 109, 11–26 (2021).
pubmed: 33412093
pmcid: 7808568
doi: 10.1016/j.neuron.2020.12.010
Lister, R. et al. Global epigenomic reconfiguration during mammalian brain development. Science 341, 1237905 (2013).
pubmed: 23828890
pmcid: 3785061
doi: 10.1126/science.1237905
Zoghbi, H. Y. & Beaudet, A. L. Epigenetics and human disease. Cold Spring Harb. Perspect. Biol. 8, a019497 (2016).
pubmed: 26834142
pmcid: 4743078
doi: 10.1101/cshperspect.a019497
He, Y. & Ecker, J. R. Non-CG methylation in the human genome. Annu. Rev. Genomics Hum. Genet. 16, 55–77 (2015).
pubmed: 26077819
pmcid: 4729449
doi: 10.1146/annurev-genom-090413-025437
Luo, C., Hajkova, P. & Ecker, J. R. Dynamic DNA methylation: in the right place at the right time. Science 361, 1336–1340 (2018).
pubmed: 30262495
pmcid: 6197482
doi: 10.1126/science.aat6806
Guo, J. U. et al. Distribution, recognition and regulation of non-CpG methylation in the adult mammalian brain. Nat. Neurosci. 17, 215–222 (2014).
pubmed: 24362762
doi: 10.1038/nn.3607
Gabel, H. W. et al. Disruption of DNA-methylation-dependent long gene repression in Rett syndrome. Nature 522, 89–93 (2015).
pubmed: 25762136
pmcid: 4480648
doi: 10.1038/nature14319
Lagger, S. et al. MeCP2 recognizes cytosine methylated tri-nucleotide and di-nucleotide sequences to tune transcription in the mammalian brain. PLoS Genet. 13, e1006793 (2017).
pubmed: 28498846
pmcid: 5446194
doi: 10.1371/journal.pgen.1006793
Chen, L. et al. MeCP2 binds to non-CG methylated DNA as neurons mature, influencing transcription and the timing of onset for Rett syndrome. Proc. Natl Acad. Sci. USA 112, 5509–5514 (2015).
pubmed: 25870282
pmcid: 4418849
doi: 10.1073/pnas.1505909112
Tillotson, R. & Bird, A. The molecular basis of MeCP2 function in the brain. J. Mol. Biol. https://doi.org/10.1016/j.jmb.2019.10.004 (2019).
He, Y. et al. Spatiotemporal DNA methylome dynamics of the developing mouse fetus. Nature 583, 752–759 (2020).
pubmed: 32728242
pmcid: 7398276
doi: 10.1038/s41586-020-2119-x
Kim, S. & Wysocka, J. Deciphering the multi-scale, quantitative cis-regulatory code. Mol. Cell 83, 373–392 (2023).
pubmed: 36693380
doi: 10.1016/j.molcel.2022.12.032
Luo, C. et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017).
pubmed: 28798132
pmcid: 5570439
doi: 10.1126/science.aan3351
Luo, C. et al. Robust single-cell DNA methylome profiling with snmC-seq2. Nat. Commun. 9, 3824 (2018).
pubmed: 30237449
pmcid: 6147798
doi: 10.1038/s41467-018-06355-2
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).
pubmed: 25858977
pmcid: 4662681
doi: 10.1126/science.aaa6090
Ming, G.-L. & Song, H. Adult neurogenesis in the mammalian brain: significant answers and significant questions. Neuron 70, 687–702 (2011).
pubmed: 21609825
pmcid: 3106107
doi: 10.1016/j.neuron.2011.05.001
Zeng, H. What is a cell type and how to define it? Cell 185, 2739–2755 (2022).
pubmed: 35868277
pmcid: 9342916
doi: 10.1016/j.cell.2022.06.031
Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).
pubmed: 34725479
pmcid: 9255697
doi: 10.1038/s41592-021-01282-5
Zhang, M. et al. Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH. Nature 598, 137–143 (2021).
pubmed: 34616063
pmcid: 8494645
doi: 10.1038/s41586-021-03705-x
Nano, P. R., Nguyen, C. V., Mil, J. & Bhaduri, A. Cortical cartography: mapping arealization using single-cell omics technology. Front. Neural Circuits 15, 788560 (2021).
pubmed: 34955761
pmcid: 8707733
doi: 10.3389/fncir.2021.788560
Berto, S., Usui, N., Konopka, G. & Fogel, B. L. ELAVL2-regulated transcriptional and splicing networks in human neurons link neurodevelopment and autism. Hum. Mol. Genet. 25, 2451–2464 (2016).
pubmed: 27260404
pmcid: 6086562
Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).
pubmed: 19815776
pmcid: 2858594
doi: 10.1126/science.1181369
Ladd, A. N. CUG-BP, Elav-like family (CELF)-mediated alternative splicing regulation in the brain during health and disease. Mol. Cell. Neurosci. 56, 456–464 (2013).
pubmed: 23247071
doi: 10.1016/j.mcn.2012.12.003
Xie, Z. et al. Gene set knowledge discovery with Enrichr. Curr. Protoc. 1, e90 (2021).
pubmed: 33780170
pmcid: 8152575
doi: 10.1002/cpz1.90
La Manno, G. et al. Molecular architecture of the developing mouse brain. Nature https://doi.org/10.1038/s41586-021-03775-x (2021).
Tan, L. et al. Lifelong restructuring of 3D genome architecture in cerebellar granule cells. Science 381, 1112–1119 (2023).
pubmed: 37676945
doi: 10.1126/science.adh3253
Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).
pubmed: 22495300
pmcid: 3356448
doi: 10.1038/nature11082
Vilariño-Güell, C. et al. LINGO1 and LINGO2 variants are associated with essential tremor and Parkinson disease. Neurogenetics 11, 401–408 (2010).
pubmed: 20369371
pmcid: 3930084
doi: 10.1007/s10048-010-0241-x
Rao, S. S. P. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).
pubmed: 25497547
pmcid: 5635824
doi: 10.1016/j.cell.2014.11.021
Kamimoto, K. et al. Dissecting cell identity via network inference and in silico gene perturbation. Nature https://doi.org/10.1038/s41586-022-05688-9 (2023).
Bravo González-Blas, C. et al. SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks. Nat. Methods https://doi.org/10.1038/s41592-023-01938-4 (2023).
Mukamel, Z. et al. Regulation of MET by FOXP2, genes implicated in higher cognitive dysfunction and autism risk. J. Neurosci. 31, 11437–11442 (2011).
pubmed: 21832174
pmcid: 3667610
doi: 10.1523/JNEUROSCI.0181-11.2011
Duclot, F. & Kabbaj, M. The role of early growth response 1 (EGR1) in brain plasticity and neuropsychiatric disorders. Front. Behav. Neurosci. 11, 35 (2017).
pubmed: 28321184
pmcid: 5337695
doi: 10.3389/fnbeh.2017.00035
Hobert, O. & Kratsios, P. Neuronal identity control by terminal selectors in worms, flies, and chordates. Curr. Opin. Neurobiol. 56, 97–105 (2019).
pubmed: 30665084
doi: 10.1016/j.conb.2018.12.006
Zhang, Z. et al. Epigenomic diversity of cortical projection neurons in the mouse brain. Nature 598, 167–173 (2021).
pubmed: 34616065
pmcid: 8494636
doi: 10.1038/s41586-021-03223-w
Joung, J. et al. A transcription factor atlas of directed differentiation. Cell 186, 209–229.e26 (2023).
pubmed: 36608654
doi: 10.1016/j.cell.2022.11.026
Porter, R. S., Jaamour, F. & Iwase, S. Neuron-specific alternative splicing of transcriptional machineries: implications for neurodevelopmental disorders. Mol. Cell. Neurosci. 87, 35–45 (2018).
pubmed: 29254826
doi: 10.1016/j.mcn.2017.10.006
Linker, S. M. et al. Combined single-cell profiling of expression and DNA methylation reveals splicing regulation and heterogeneity. Genome Biol. 20, 30 (2019).
pubmed: 30744673
pmcid: 6371455
doi: 10.1186/s13059-019-1644-0
Booeshaghi, A. S. et al. Isoform cell-type specificity in the mouse primary motor cortex. Nature 598, 195–199 (2021).
pubmed: 34616073
pmcid: 8494650
doi: 10.1038/s41586-021-03969-3
Südhof, T. C. Synaptic neurexin complexes: a molecular code for the logic of neural circuits. Cell 171, 745–769 (2017).
pubmed: 29100073
pmcid: 5694349
doi: 10.1016/j.cell.2017.10.024
Chen, Z. et al. Genetic association of neurotrophic tyrosine kinase receptor type 2 (NTRK2) with Alzheimer’s disease. Am. J. Med. Genet. B Neuropsychiatr. Genet. 147, 363–369 (2008).
pubmed: 17918233
doi: 10.1002/ajmg.b.30607
Murphy, K. C. & Volkert, M. R. Structural/functional analysis of the human OXR1 protein: identification of exon 8 as the anti-oxidant encoding function. BMC Mol. Biol. 13, 26 (2012).
pubmed: 22873401
pmcid: 3462732
doi: 10.1186/1471-2199-13-26
Bhat, P., Honson, D. & Guttman, M. Nuclear compartmentalization as a mechanism of quantitative control of gene expression. Nat. Rev. Mol. Cell Biol. 22, 653–670 (2021).
pubmed: 34341548
doi: 10.1038/s41580-021-00387-1
Wu, H., Zhang, J., Tan, L. & Xie, X. S. Extruding transcription elongation loops observed in high-resolution single-cell 3D genomes. Preprint at bioRxiv https://doi.org/10.1101/2023.02.18.529096 (2023).
Tarjan, D. R., Flavahan, W. A. & Bernstein, B. E. Epigenome editing strategies for the functional annotation of CTCF insulators. Nat. Commun. 10, 4258 (2019).
pubmed: 31534142
pmcid: 6751197
doi: 10.1038/s41467-019-12166-w
Van de Sande, B. et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat. Protoc. 15, 2247–2276 (2020).
pubmed: 32561888
doi: 10.1038/s41596-020-0336-2
Allen Institute for Brain Science. Allen Mouse Brain Reference Atlas CCFv3. Allen Brain Atlas http://atlas.brain-map.org (2017).
Yushkevich, P. A. et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31, 1116–1128 (2006).
pubmed: 16545965
doi: 10.1016/j.neuroimage.2006.01.015
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).
doi: 10.14806/ej.17.1.200
Krueger, F. & Andrews, S. R. Bismark: a flexible aligner and methylation caller for bisulfite-seq applications. Bioinformatics 27, 1571–1572 (2011).
pubmed: 21493656
pmcid: 3102221
doi: 10.1093/bioinformatics/btr167
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
pubmed: 22388286
pmcid: 3322381
doi: 10.1038/nmeth.1923
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
pubmed: 19505943
pmcid: 2723002
doi: 10.1093/bioinformatics/btp352
Mölder, F. et al. Sustainable data analysis with Snakemake. F1000Research 10, 33 (2021).
pubmed: 34035898
pmcid: 8114187
doi: 10.12688/f1000research.29032.2
Miles, A. et al. zarr-developers/zarr-python: v2.5.0. Zenodo https://doi.org/10.5281/zenodo.4069231 (2020).
Hoyer, S. & Hamman, J. J. xarray: N-D labeled Arrays and Datasets in Python. J. Open Res. Softw. https://doi.org/10.5334/jors.148 (2017).
Rocklin, M. Dask: Parallel computation with blocked algorithms and task scheduling. In Proc. 14th Python in Science Conference (eds Huff, K. & Bergstra, J.) 126–132 (Citeseer, 2015).
Yang, Z. et al. SkyPilot: an intercloud broker for sky computing. In Proc. 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’23) 437–455 (USENIX, 2023).
Zhou, J. et al. Robust single-cell Hi-C clustering by convolution- and random-walk-based imputation. Proc. Natl Acad. Sci. USA 116, 14011–14018 (2019).
pubmed: 31235599
pmcid: 6628819
doi: 10.1073/pnas.1901423116
Li, H. Tabix: fast retrieval of sequence features from generic TAB-delimited files. Bioinformatics 27, 718–719 (2011).
pubmed: 21208982
pmcid: 3042176
doi: 10.1093/bioinformatics/btq671
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B Stat. Methodol. 57, 289–300 (1995).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
pubmed: 29409532
pmcid: 5802054
doi: 10.1186/s13059-017-1382-0
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE Blacklist: identification of problematic regions of the genome. Sci. Rep. 9, 9354 (2019).
pubmed: 31249361
pmcid: 6597582
doi: 10.1038/s41598-019-45839-z
Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014).
pubmed: 25042786
pmcid: 4117646
doi: 10.1038/nmeth.3035
Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014.e22 (2018).
pubmed: 30096314
pmcid: 6086934
doi: 10.1016/j.cell.2018.06.021
van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).
Poličar, P. G., Stražar, M. & Zupan, B. openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding. Preprint at bioRxiv https://doi.org/10.1101/731877 (2019).
Kobak, D. & Berens, P. The art of using t-SNE for single-cell transcriptomics. Nat. Commun. 10, 5416 (2019).
pubmed: 31780648
pmcid: 6882829
doi: 10.1038/s41467-019-13056-x
Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).
pubmed: 30914743
pmcid: 6435756
doi: 10.1038/s41598-019-41695-z
Miao, Z. et al. Putative cell type discovery from single-cell gene expression data. Nat. Methods 17, 621–628 (2020).
pubmed: 32424270
doi: 10.1038/s41592-020-0825-9
Schultz, M. D. et al. Human body epigenome maps reveal noncanonical DNA methylation variation. Nature 523, 212–216 (2015).
pubmed: 26030523
pmcid: 4499021
doi: 10.1038/nature14465
Hodge, R. D. et al. Conserved cell types with divergent features in human versus mouse cortex. Nature 573, 61–68 (2019).
pubmed: 31435019
pmcid: 6919571
doi: 10.1038/s41586-019-1506-7
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
pubmed: 29608179
pmcid: 6700744
doi: 10.1038/nbt.4096
Abdennur, N. & Mirny, L. A. Cooler: scalable storage for Hi-C data and other genomically labeled arrays. Bioinformatics 36, 311–316 (2020).
pubmed: 31290943
doi: 10.1093/bioinformatics/btz540
van der Sande, M. & van Heeringen, S. qnorm (version v0.6.1). Zenodo https://doi.org/10.5281/zenodo.4114608 (2020).
Shin, H. et al. TopDom: an efficient and deterministic method for identifying topological domains in genomes. Nucleic Acids Res. 44, e70 (2016).
pubmed: 26704975
doi: 10.1093/nar/gkv1505
Xie, F. et al. Robust enhancer-gene regulation identified by single-cell transcriptomes and epigenomes. Cell Genom. 3, 100342 (2023).
Ramírez, F., Dündar, F., Diehl, S., Grüning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).
pubmed: 24799436
pmcid: 4086134
doi: 10.1093/nar/gku365
Gupta, S., Stamatoyannopoulos, J. A., Bailey, T. L. & Noble, W. S. Quantifying similarity between motifs. Genome Biol. 8, R24 (2007).
pubmed: 17324271
pmcid: 1852410
doi: 10.1186/gb-2007-8-2-r24
Frith, M. C., Li, M. C. & Weng, Z. Cluster-Buster: finding dense clusters of motifs in DNA sequences. Nucleic Acids Res. 31, 3666–3668 (2003).
pubmed: 12824389
pmcid: 168947
doi: 10.1093/nar/gkg540
Zhang, K., Wang, M., Zhao, Y. & Wang, W. Taiji: system-level identification of key transcription factors reveals transcriptional waves in mouse embryonic development. Sci. Adv. 5, eaav3262 (2019).
pubmed: 30944857
pmcid: 6436936
doi: 10.1126/sciadv.aav3262
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886
doi: 10.1093/bioinformatics/bts635
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
pubmed: 27043002
doi: 10.1038/nbt.3519
Smith, S. J., Hawrylycz, M., Rossier, J. & Sümbül, U. New light on cortical neuropeptides and synaptic network plasticity. Curr. Opin. Neurobiol. 63, 176–188 (2020).
pubmed: 32679509
doi: 10.1016/j.conb.2020.04.002